Privacy-preserving neural network based on multi-party computation (MPC) is currently one of the main methods to solve this problem, which involves Secret Sharing[1]}, Garbled Circuit[2]}, Oblivious Transfer[3]}, [4]}, Homomorphic Encryption[5]}, [6]}, [7]}, [8]}, [9]}, and other cryptographic knowledge. SecureML[10]}, ABY\(^3\)[11]}, SecureNN[12]}, and FALCON[13]} have all performed considerable research in this area and made outstanding contributions to the advancement of this technology. The framework construction of this method involves many situations, such as 2PC (two-party computation)[10]}, 3PC (three-parties computation)[11]}, [12]}, [13]}, [18]}, [19]}, and even Multi-PC (multiple-parties computation) [20]}. Although Multi-PC has improved computation or communication efficiency, there are still problems. The existence of multiple parties makes it more difficult to ensure that all participants do not collude with each other, and there are difficulties in the actual deployment. The current mainstream 3PC or Multi-PC security model must guarantee an honest majority, but this requirement cannot be easily satisfied in the real situation. Even if it is deployed in cloud servers belonging to different entities, the collusion of parties cannot be easily controlled. However, 2PC can meet this condition. In the above example, the servers can be separately deployed in the two companies. Because they have a naturally hostile relationship, collusion with each other is contrary to their respective interests, so there is no motive for collusion. Accordingly, 2PC can also be extended to Multi-PC, as long as each party divides the data and sends them to the two servers. However, for 3PC or Multi-PC, this kind of naturally hostile security guarantee does not exist. Therefore, we believe that the 2PC-based privacy-preserving neural network is still the mainstream in this direction.
